# Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models

arXiv: https://arxiv.org/abs/2508.13678 · HTML: https://mlsanity.com/paper/2508.13678/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 8 of 8)

- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [From Statistical Relational to Neuro-Symbolic Artificial Intelligence](https://mlsanity.com/paper/2003.08316/) (2020)
- [Plansformer: Generating Symbolic Plans using Transformers](https://mlsanity.com/paper/2212.08681/) (2022)
- [ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents](https://mlsanity.com/paper/2412.13682/) (2024)
- [RLSF: Fine-tuning LLMs via Symbolic Feedback](https://mlsanity.com/paper/2405.16661/) (2024)
- [LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM](https://mlsanity.com/paper/2502.06572/) (2025)
- [DiLA: Enhancing LLM Tool Learning with Differential Logic Layer](https://mlsanity.com/paper/2402.11903/) (2024)
- [AlphaIntegrator: Transformer Action Search for Symbolic Integration Proofs](https://mlsanity.com/paper/2410.02666/) (2024)

## Built on by (most cited first, 20 of 20)

- [Adaptive Information Control for Search-Augmented LLM Reasoning](https://mlsanity.com/paper/2602.01672/) (2026)
- [SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning](https://mlsanity.com/paper/2602.01990/) (2026)
- [Architecting Trust in Artificial Epistemic Agents](https://mlsanity.com/paper/2603.02960/) (2026)
- [The Y-Combinator for LLMs: Solving Long-Context Rot with λ-Calculus](https://mlsanity.com/paper/2603.20105/) (2026)
- [Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2603.24004/) (2026)
- [Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents](https://mlsanity.com/paper/2604.00555/) (2026)
- [NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing](https://mlsanity.com/paper/2603.16307/) (2026)
- [Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2](https://mlsanity.com/paper/2603.20334/) (2026)
- [Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents](https://mlsanity.com/paper/2604.02734/) (2026)
- [Hierarchical Alignment: Enforcing Hierarchical Instruction-Following in LLMs through Logical Consistency](https://mlsanity.com/paper/2604.09075/) (2026)
- [Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework](https://mlsanity.com/paper/2606.27757/) (2026)
- [sLTN: Structural Logic Tensor Networks](https://mlsanity.com/paper/2608.11136/) (2026)
- [Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models](https://mlsanity.com/paper/2510.20477/) (2025)
- [A Progressive Visual-Logic-Aligned Framework for Ride-Hailing Adjudication](https://mlsanity.com/paper/2603.17328/) (2026)
- [Simulating Human Cognition: Heartbeat-Driven Autonomous Thinking Activity Scheduling for LLM-based AI systems](https://mlsanity.com/paper/2604.14178/) (2026)
- [BiNSGPS: Geometry Problem Solving via Bidirectional Neuro-Symbolic Interaction](https://mlsanity.com/paper/2606.04648/) (2026)
- [Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning](https://mlsanity.com/paper/2606.12910/) (2026)
- [SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification](https://mlsanity.com/paper/2608.08786/) (2026)
- [SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data](https://mlsanity.com/paper/2608.28408/) (2026)
- [Euler: Towards Human-Centered Multi-Agent Mathematical Discovery](https://mlsanity.com/paper/2610.19864/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown promising results across various tasks, yet their reasoning capabilities remain a fundamental challenge. Developing AI systems with strong reasoning capabilities is regarded as a crucial milestone in the pursuit of Artificial General Intelligence (AGI) and has garnered considerable attention from both academia and industry. Various techniques have been explored to enhance the reasoning capabilities of LLMs, with neuro-symbolic approaches being a particularly promising way. This paper comprehensively reviews recent developments in neuro-symbolic approaches for enhancing LLM reasoning. We first present a formalization of reasoning tasks and give a brief introduction to the neurosymbolic learning paradigm. Then, we discuss neuro-symbolic methods for improving the reasoning capabilities of LLMs from three perspectives: Symbolic->LLM, LLM->Symbolic, and LLM+Symbolic. Finally, we discuss several key challenges and promising future directions. We have also released a GitHub repository including papers and resources related to this survey: https://github.com/LAMDASZ-ML/Awesome-LLM-Reasoning-with-NeSy.